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library(dplyr)
library(ggplot2)
library(tidyr)
library(car)
library(MASS)
library(repr)
library(pals)
getwd()
[1] "C:/Users/Natalie/git_proj/Is-your-electric-car-overpriced-"
ecars_raw = ecars_raw %>% rename(Price = Price.DE., Acceleration = acceleration..0.100.)
Error in `rename()`:
! Can't rename columns that don't exist.
✖ Column `Price.DE.` doesn't exist.
Backtrace:
1. ecars_raw %>% rename(Price = Price.DE., Acceleration = acceleration..0.100.)
3. dplyr:::rename.data.frame(., Price = Price.DE., Acceleration = acceleration..0.100.)
make = strsplit(ecars_raw$Car_name, split = ' ')
make_ = c()
n = length(make)
for (i in 1:n) {
make_[i] = make[[i]][1]
}
ecars_raw$Make = make_
ecars_raw = ecars_raw %>% relocate(Make, .before = Car_name_link)
ecars_raw = ecars_raw %>% relocate(Battery, .after = Car_name_link)
ecars_raw
make_colors = c('#e6194b', '#f58231', '#ffe119',
'#bcf60c','#3cb44b', '#008080',
'#aaffc3', '#4363d8', '#000075',
'#46f0f0', '#911eb4', '#e6beff',
'#f032e6', '#fabebe')
summary(price_model_full)
Call:
lm(formula = Price ~ Battery + Efficiency + Fast_charge + Range +
Top_speed + Acceleration, data = ecars)
Residuals:
Min 1Q Median 3Q Max
-53557 -11739 -178 8223 84430
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.613e+05 2.398e+04 -6.724 8.91e-11 ***
Battery 2.215e+02 3.506e+02 0.632 0.52798
Efficiency 3.052e+02 1.140e+02 2.678 0.00781 **
Fast_charge 1.785e+01 7.819e+00 2.283 0.02315 *
Range 9.066e+00 6.679e+01 0.136 0.89211
Top_speed 7.013e+02 7.086e+01 9.897 < 2e-16 ***
Acceleration 1.762e+03 7.746e+02 2.274 0.02366 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 18530 on 300 degrees of freedom
Multiple R-squared: 0.7167, Adjusted R-squared: 0.711
F-statistic: 126.5 on 6 and 300 DF, p-value: < 2.2e-16
forwardAIC = step(price_model_empty, scope, direction = 'forward', k = 2)
Start: AIC=6415.84
Price ~ 1
Df Sum of Sq RSS AIC
+ Top_speed 1 2.1023e+11 1.5319e+11 6152.6
+ Battery 1 1.7911e+11 1.8431e+11 6209.4
+ Fast_charge 1 1.3923e+11 2.2419e+11 6269.5
+ Range 1 1.2615e+11 2.3728e+11 6287.0
+ Acceleration 1 1.0296e+11 2.6046e+11 6315.6
+ Efficiency 1 1.1067e+10 3.5235e+11 6408.3
<none> 3.6342e+11 6415.8
Step: AIC=6152.62
Price ~ Top_speed
Df Sum of Sq RSS AIC
+ Efficiency 1 4.2802e+10 1.1039e+11 6054.0
+ Battery 1 1.9965e+10 1.3322e+11 6111.8
+ Acceleration 1 1.5065e+10 1.3812e+11 6122.8
<none> 1.5319e+11 6152.6
+ Fast_charge 1 2.2387e+08 1.5297e+11 6154.2
+ Range 1 7.0491e+07 1.5312e+11 6154.5
Step: AIC=6054.03
Price ~ Top_speed + Efficiency
Df Sum of Sq RSS AIC
+ Fast_charge 1 3519695484 1.0687e+11 6046.1
+ Range 1 3506122724 1.0688e+11 6046.1
+ Battery 1 3063673448 1.0732e+11 6047.4
+ Acceleration 1 915987463 1.0947e+11 6053.5
<none> 1.1039e+11 6054.0
Step: AIC=6046.08
Price ~ Top_speed + Efficiency + Fast_charge
Df Sum of Sq RSS AIC
+ Range 1 2137906570 1.0473e+11 6041.9
+ Battery 1 2032210165 1.0484e+11 6042.2
+ Acceleration 1 713779942 1.0615e+11 6046.0
<none> 1.0687e+11 6046.1
Step: AIC=6041.87
Price ~ Top_speed + Efficiency + Fast_charge + Range
Df Sum of Sq RSS AIC
+ Acceleration 1 1639080066 1.0309e+11 6039.0
<none> 1.0473e+11 6041.9
+ Battery 1 1076868 1.0473e+11 6043.9
Step: AIC=6039.03
Price ~ Top_speed + Efficiency + Fast_charge + Range + Acceleration
Df Sum of Sq RSS AIC
<none> 1.0309e+11 6039.0
+ Battery 1 1.37e+08 1.0295e+11 6040.6
summary(price_model)
Call:
lm(formula = Price_lambda ~ Efficiency + Fast_charge + Range +
Top_speed + Acceleration, data = ecars)
Residuals:
Min 1Q Median 3Q Max
-2.951e-04 -4.127e-05 8.710e-06 4.794e-05 1.467e-04
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.413e+00 5.206e-05 27130.409 < 2e-16 ***
Efficiency 3.059e-06 1.332e-07 22.957 < 2e-16 ***
Fast_charge 1.407e-07 2.675e-08 5.259 2.75e-07 ***
Range 5.510e-07 5.805e-08 9.491 < 2e-16 ***
Top_speed 1.601e-06 2.455e-07 6.524 2.89e-10 ***
Acceleration -4.715e-06 2.603e-06 -1.812 0.071 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 6.436e-05 on 301 degrees of freedom
Multiple R-squared: 0.853, Adjusted R-squared: 0.8505
F-statistic: 349.3 on 5 and 301 DF, p-value: < 2.2e-16
prediction = predict(price_model, ecars, interval = 'prediction')
confidence = predict(price_model, ecars, interval = 'confidence')
prediction_dollars = ((prediction * lambda) + 1)^(1/lambda)
confidence_dollars = ((confidence * lambda) + 1)^(1/lambda)
predicted_price = data.frame(Name = ecars$Car_name,
Make = ecars$Make,
Price = ecars$Price/1000,
Predicted = (prediction_dollars[,1]/1000),
Predict_lwr = (prediction_dollars[,2]/1000),
Predict_upr = (prediction_dollars[,3]/1000),
Confidence_lwr = (confidence_dollars[,2]/1000),
Confidence_upr = (confidence_dollars[,3]/1000))
predicted_price
ggplot(NULL, aes(Predicted_price, Price)) +
geom_smooth(data = predicted_price, aes(x = Predicted, y = Predicted), col = 'black', linetype = 'dashed') +
geom_smooth(data = predicted_price, aes(x = Predicted, y = Predict_lwr), col = 'red', linetype = 'dashed') +
geom_smooth(data = predicted_price, aes(x = Predicted, y = Predict_upr), col = 'red') +
geom_smooth(data = predicted_price, aes(x = Predicted, y = Confidence_lwr), col = 'blue') +
geom_smooth(data = predicted_price, aes(x = Predicted, y = Confidence_upr), col = 'blue') +
geom_ribbon(aes(ymin = predicted_price$Confidence_lwr, ymax = predicted_price$Confidence_upr), fill = "grey70")+
ylim(25,150) + xlim(25, 140) +
geom_point(data = predicted_price, aes(x = Predicted, y = Price), alpha = .5) +
geom_point(data = most_makes, aes(x = mean_predicted, y = mean_price, col = Make), size = 3) +
scale_color_manual(values = make_colors) +
xlab("Predicted Price in 1000s of Euros") + ylab("Price in 1000s of Euros") +
ggtitle('Price of Electric Vehicles with mean cost per Make')
Error in `geom_ribbon()`:
! Problem while computing aesthetics.
ℹ Error occurred in the 6th layer.
Caused by error:
! object 'Predicted_price' not found
Backtrace:
1. base (local) `<fn>`(x)
2. ggplot2:::print.ggplot(x)
4. ggplot2:::ggplot_build.ggplot(x)
5. ggplot2:::by_layer(...)
12. ggplot2 (local) f(l = layers[[i]], d = data[[i]])
13. l$compute_aesthetics(d, plot)
14. ggplot2 (local) compute_aesthetics(..., self = self)
15. base::lapply(aesthetics, eval_tidy, data = data, env = env)
16. rlang (local) FUN(X[[i]], ...)



testa = ggplot(NULL, aes(Predicted_price, Price)) +
geom_smooth(data = predicted_price, aes(x = Predicted, y = Predicted),
col = 'blue', size = .8, alpha = .5) +
geom_smooth(data = predicted_price, aes(x = Predicted, y = Predict_lwr),
col = 'red', linetype = 'dashed', size = .8, alpha = .8) +
geom_smooth(data = predicted_price, aes(x = Predicted, y = Predict_upr),
col = 'red', linetype = 'dashed',size = .8, alpha = .8) +
geom_smooth(data = predicted_price, aes(x = Predicted, y = Confidence_lwr),
col = 'black', linetype = 'dashed', size = .8, alpha = .8) +
geom_smooth(data = predicted_price, aes(x = Predicted, y = Confidence_upr),
col = 'black', linetype = 'dashed', size = .8, alpha = .8) +
geom_point(data = predicted_price, aes(x = Predicted, y = Price, text = Name), alpha = .5) +
theme(legend.position = "right", legend.text = element_text(size = 8))+
ylim(25, 250) + xlim(25, 250) +
labs(title = "Predicted Price vs. Price for all EV Models",
caption = "Data source: ToothGrowth",
x = "Predicted price (euros in thousands)", y = "German Price (euros in thousands)",
tag = "A")
Warning: Ignoring unknown aesthetics: text
ggplotly(testa, tooltip = c("x", 'y', "text"))
`geom_smooth()` using method = 'loess' and formula = 'y ~ x'`geom_smooth()` using method = 'loess' and formula = 'y ~ x'Warning: Removed 4 rows containing non-finite values (`stat_smooth()`).`geom_smooth()` using method = 'loess' and formula = 'y ~ x'Warning: Removed 8 rows containing non-finite values (`stat_smooth()`).`geom_smooth()` using method = 'loess' and formula = 'y ~ x'Warning: Removed 1 rows containing non-finite values (`stat_smooth()`).`geom_smooth()` using method = 'loess' and formula = 'y ~ x'Warning: Removed 1 rows containing non-finite values (`stat_smooth()`).

unique(ecars_missing_price$Make)
[1] "Rolls-Royce" "Hongqi" "Audi" "Peugeot" "Mercedes" "Opel" "Polestar" "Hyundai"
[9] "Volkswagen" "XPENG" "CUPRA" "Genesis" "Maserati" "Mini" "Seres" "Volvo"
[17] "Ford" "Lexus" "Skoda" "Fiat" "Kia" "Toyota"




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---
title: "R Notebook"
output: html_notebook
---

This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. When you execute code within the notebook, the results appear beneath the code. 

Try executing this chunk by clicking the *Run* button within the chunk or by placing your cursor inside it and pressing *Ctrl+Shift+Enter*. 

```{r}
library(dplyr)
library(ggplot2)
library(tidyr)
library(car)
library(MASS)
library(repr)
library(pals)
```
```{r}
getwd()
ecars_raw = read.csv('EV_cars.csv')

```
```{r}
ecars_raw = ecars_raw %>% rename(Price = Price.DE., Acceleration = acceleration..0.100.)
```

```{r}
make = strsplit(ecars_raw$Car_name, split = ' ')

make_ = c()
n = length(make)

for (i in 1:n) {
  make_[i] = make[[i]][1]
}

ecars_raw$Make = make_
```


```{r}
ecars_raw = ecars_raw %>% relocate(Make, .before = Car_name_link)
ecars_raw = ecars_raw %>% relocate(Battery, .after = Car_name_link)
ecars_raw
```

```{r}
ecars_raw = ecars_raw %>% filter(!is.na(Fast_charge))
ecars = ecars_raw %>% filter(!is.na(Price))
ecars_missing_price = ecars_raw %>% filter(is.na(Price))
write.csv(ecars,file='/Users/Natalie/git_proj/Is-your-electric-car-overpriced-/ecars.csv', row.names=FALSE)
write.csv(most_makes,file='/Users/Natalie/git_proj/Is-your-electric-car-overpriced-/most_makes.csv', row.names=FALSE)

```
```{r}
make_colors = c('#e6194b', '#f58231',  '#ffe119', 
                '#bcf60c','#3cb44b', '#008080',
                '#aaffc3', '#4363d8', '#000075',
                '#46f0f0', '#911eb4', '#e6beff',
                '#f032e6', '#fabebe')
```

```{r}
price_model_empty = lm(Price ~ 1, data = ecars)
price_model_full= lm(Price ~ Battery + Efficiency + Fast_charge + Range + Top_speed + Acceleration, data = ecars)
summary(price_model_full)
```
```{r}
ecars_missing_price
```

```{r}
scope = list(lower = formula(price_model_empty), upper = formula(price_model_full))
forwardAIC = step(price_model_empty, scope, direction = 'forward', k = 2)
```

```{r}
price_model_initial = lm(Price ~ Efficiency + Fast_charge + Range + Top_speed + Acceleration, data = ecars)
summary(price_model_initial)
bc = boxCox(price_model_initial)
lambda = bc$x[which(bc$y == max(bc$y))]
ecars$Price_lambda = (ecars$Price^lambda - 1)/lambda
price_model = lm(Price_lambda ~ Efficiency + Fast_charge + Range + Top_speed + Acceleration, data = ecars)
summary(price_model)
plot(price_model)
broom::glance(price_model)
```

```{r}
prediction = predict(price_model, ecars, interval = 'prediction')
confidence = predict(price_model, ecars, interval = 'confidence')
prediction_dollars = ((prediction * lambda) + 1)^(1/lambda)
confidence_dollars = ((confidence * lambda) + 1)^(1/lambda)
predicted_price = data.frame(Name = ecars$Car_name,
                             Make = ecars$Make,
                             Price = ecars$Price/1000,
                             Predicted = (prediction_dollars[,1]/1000),
                             Predict_lwr = (prediction_dollars[,2]/1000),
                             Predict_upr = (prediction_dollars[,3]/1000),
                             Confidence_lwr = (confidence_dollars[,2]/1000),
                             Confidence_upr = (confidence_dollars[,3]/1000))
predicted_price
write.csv(predicted_price,file='/Users/Natalie/git_proj/Is-your-electric-car-overpriced-/predicted_price.csv', row.names=FALSE)
```
```{r}
most_makes = predicted_price %>%
  group_by(Make)%>%
  filter(n() >= 10) %>%
  summarise(mean_price = mean(Price), mean_predicted = mean(Predicted))
most_makes
```
```{r}
predicted_price %>%
  filter(Make == 'Porsche')
```


```{r}

ggplot(NULL, aes(Predicted_price, Price)) +
      geom_smooth(data = predicted_price, aes(x = Predicted, y = Predicted), col = 'black') +
      geom_smooth(data = predicted_price, aes(x = Predicted, y = Predict_lwr), col = 'red') +
      geom_smooth(data = predicted_price, aes(x = Predicted, y = Predict_upr), col = 'red') +
      geom_smooth(data = predicted_price, aes(x = Predicted, y = Confidence_lwr), col = 'blue') +
      geom_smooth(data = predicted_price, aes(x = Predicted, y = Confidence_upr), col = 'blue') +
      ylim(0,300) +
      geom_point(data = predicted_price, aes(x = Predicted, y = Price), alpha = .5) +
      geom_point(data = most_makes, aes(x = mean_predicted, y = mean_price, col = Make), size = 3) +
      scale_color_manual(values = make_colors)

ggplot(NULL, aes(Predicted_price, Price)) +
      geom_smooth(data = predicted_price, aes(x = Predicted, y = Predicted), col = 'black', linetype = 'dashed') +
      geom_smooth(data = predicted_price, aes(x = Predicted, y = Predict_lwr), col = 'red', linetype = 'dashed') +
      geom_smooth(data = predicted_price, aes(x = Predicted, y = Predict_upr), col = 'red') +
      geom_smooth(data = predicted_price, aes(x = Predicted, y = Confidence_lwr), col = 'blue') +
      geom_smooth(data = predicted_price, aes(x = Predicted, y = Confidence_upr), col = 'blue') +
      ylim(25,150) + xlim(25, 140) +
      geom_point(data = predicted_price, aes(x = Predicted, y = Price), alpha = .5) + 
      geom_point(data = most_makes, aes(x = mean_predicted, y = mean_price, col = Make), size = 3) +
      scale_color_manual(values = make_colors) +
      xlab("Predicted Price in 1000s of Euros") + ylab("Price in 1000s of Euros") +
      ggtitle('Price of Electric Vehicles with mean cost per Make')


# geom_point(data = most_makes, aes(x = mean_price, y = mean_predicted), size = 3, shape = 23, fill = make_colors) +


```
```{r}
   
    ggplot(NULL, aes(Predicted_price, Price)) +
      geom_smooth(data = predicted_price, aes(x = Predicted, y = Predicted), 
                  col = 'blue', size = .5, alpha = .8) +
      geom_smooth(data = predicted_price, aes(x = Predicted, y = Predict_lwr), 
                  col = 'red', linetype = 'dashed', alpha = .8) +
      geom_smooth(data = predicted_price, aes(x = Predicted, y = Predict_upr), 
                  col = 'red', linetype = 'dashed', alpha = .8) +
      geom_smooth(data = predicted_price, aes(x = Predicted, y = Confidence_lwr), 
                  col = 'black', linetype = 'dashed', alpha = .8) +
      geom_smooth(data = predicted_price, aes(x = Predicted, y = Confidence_upr), 
                  col = 'black', linetype = 'dashed', alpha = .8) +
      geom_point(data = predicted_price, aes(x = Predicted, y = Price), alpha = .5) +
      geom_point(data = most_makes, aes(x = mean_predicted, y = mean_price), size = 3) +
      geom_point(data = most_makes, aes(x = mean_predicted, y = mean_price, col = Make), size = 2) +
      theme(legend.position = "bottom", legend.text = element_text(size = 8))+
      ylim(25, 250) + xlim(25, 250) +
      xlab("Predicted price (euros in thousands)") + ylab("Price (euros in thousands)") +
      ggtitle('Predicted Price vs. Price for all EV Models')  
```

```{r}
 ggplot(NULL, aes(Predicted_price, Price)) +
      geom_smooth(data = predicted_price, aes(x = Predicted, y = Predicted), 
                  col = 'blue', size = .8, alpha = .5) +
      geom_smooth(data = predicted_price, aes(x = Predicted, y = Predict_lwr), 
                  col = 'red', linetype = 'dashed', size = .8, alpha = .8) +
      geom_smooth(data = predicted_price, aes(x = Predicted, y = Predict_upr), 
                  col = 'red', linetype = 'dashed',size = .8, alpha = .8) +
      geom_smooth(data = predicted_price, aes(x = Predicted, y = Confidence_lwr), 
                  col = 'black', linetype = 'dashed', size = .8, alpha = .8) +
      geom_smooth(data = predicted_price, aes(x = Predicted, y = Confidence_upr), 
                  col = 'black', linetype = 'dashed', size = .8, alpha = .8) +
      geom_point(data = predicted_price, aes(x = Predicted, y = Price), alpha = .5) +
      geom_point(data = most_makes, aes(x = mean_predicted, y = mean_price), size = 3) +
      geom_point(data = most_makes, aes(x = mean_predicted, y = mean_price, col = Make), size = 2) +
      scale_color_manual(values = make_colors) +
      theme(legend.position = "right", legend.text = element_text(size = 8))+
      ylim(25, 140) + xlim(25, 110) +
      labs(title = "Predicted Price vs. Price for all EV Models",
           subtitle = "Mean model price for makes with 10+ models included",
           caption = "Data source: ToothGrowth",
           x = "Predicted price (euros in thousands)", y = "German Price (euros in thousands)",
           tag = "A")
```

```{r}


testa = ggplot(NULL, aes(Predicted_price, Price)) +
      geom_smooth(data = predicted_price, aes(x = Predicted, y = Predicted), 
                  col = 'blue', size = .8, alpha = .5) +
      geom_smooth(data = predicted_price, aes(x = Predicted, y = Predict_lwr), 
                  col = 'red', linetype = 'dashed', size = .8, alpha = .8) +
      geom_smooth(data = predicted_price, aes(x = Predicted, y = Predict_upr), 
                  col = 'red', linetype = 'dashed',size = .8, alpha = .8) +
      geom_smooth(data = predicted_price, aes(x = Predicted, y = Confidence_lwr), 
                  col = 'black', linetype = 'dashed', size = .8, alpha = .8) +
      geom_smooth(data = predicted_price, aes(x = Predicted, y = Confidence_upr), 
                  col = 'black', linetype = 'dashed', size = .8, alpha = .8) +
      geom_point(data = predicted_price, aes(x = Predicted, y = Price, text = Name), alpha = .5) +
      theme(legend.position = "right", legend.text = element_text(size = 8))+
      ylim(25, 250) + xlim(25, 250) +
      labs(title = "Predicted Price vs. Price for all EV Models",
           caption = "Data source: ToothGrowth",
           x = "Predicted price (euros in thousands)", y = "German Price (euros in thousands)",
           tag = "A")

ggplotly(testa, tooltip = c("x", 'y', "text")) 
```

```{r}
ecars
ggplot(data = ecars, aes(x = Battery, y = Price)) + 
  geom_point()
```

```{r}
unique(ecars_missing_price$Make)

```

```{r}
prediction_missing = predict(price_model, ecars_missing_price, interval = 'prediction')
prediction_missing
prediction_missing_dollars = ((prediction_missing * lambda) + 1)^(1/lambda)

predicted_missing_price = data.frame(Name = ecars_missing_price$Car_name,
                             Make = ecars_missing_price$Make,
                             Predicted = (prediction_missing_dollars[,1]/1000))
                           
predicted_missing_price
```

```{r}
predicted_missing_price %>%
  group_by(Make) %>%
  filter(n()>=3)

predicted_missing_price %>%
  filter(Make == 'Rolls-Royce')
```

```{r}
summary(price_model)
plot(price_model)
saveRDS(price_model, "model.rds")
```

```{r}
                                                                 
```

```{r}

```

```{r}

```

```{r}

```

```{r}

```

```{r}

```

```{r}

```

```{r}

```

Add a new chunk by clicking the *Insert Chunk* button on the toolbar or by pressing *Ctrl+Alt+I*.

When you save the notebook, an HTML file containing the code and output will be saved alongside it (click the *Preview* button or press *Ctrl+Shift+K* to preview the HTML file).

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